Image processing apparatus and image processing method

ABSTRACT

An image processing apparatus includes an alignment unit configured to align a first fundus oculi image that is an aberration-corrected image of an eye being examined and a second fundus oculi image having a larger view angle and a lower resolution than the first fundus oculi image, by using a third fundus oculi image having a smaller view angle and a higher resolution than the second fundus oculi image; a distance acquisition unit configured to acquire a distance from a macula lutea of the eye being examined to a certain position in the first fundus oculi image aligned by the alignment unit; and an evaluation unit configured to evaluate the state of the eye being examined from the distance and information concerning photoreceptor cells included in the first fundus oculi image.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of prior U.S. patent application Ser.No. 14/140,657, filed Dec. 26, 2013 which claims the benefit of JapanesePatent Application No. 2012-287252 filed Dec. 28, 2012. U.S. patentapplication Ser. No. 14/140,657 and Japanese Patent Application No.2012-287252 are hereby incorporated by reference herein in theirentirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The embodiments disclosed herein relate to an image processing apparatusand an image processing method.

2. Description of the Related Art

For the purpose of early diagnosis of lifestyle diseases or diseasesthat rank high as a cause of blindness, the inspection of the fundusoculi of the human eye has been widely used. A scanning laserophthalmoscope (SLO), which is an ophthalmological instrument thatutilizes the principle of confocal laser scanning microscopy, is adevice that uses laser light, which serves as measurement light, toraster-scan the fundus oculi to quickly obtain a high-resolutiontwo-dimensional image from the intensity of the return light of thelaser light. In recent years, adaptive optics SLOs (AO-SLOs) includingan AO system configured to measure the aberrations of the eye beingexamined in real time by using a wavefront sensor and to correct theaberration of the measurement light that occurs in the eye beingexamined or the aberration of the return light of the measurement lightby using a wavefront correction device have been developed. The AO-SLOsfacilitate the acquisition of high-lateral-resolution two-dimensionalimages (hereinafter sometimes referred to as “AO-SLO images”). Inaddition, the photoreceptor cells in the retina are extracted fromobtained two-dimensional retinal images, and the density or distributionof the photoreceptor cells is analyzed to attempt the diagnosis of adisease or the evaluation of drug response.

Image processing for detecting the photoreceptor cells may be performedwith high accuracy by utilizing medical knowledge of photoreceptorcells. For example, it is known that the density of photoreceptor cellsdecreases as the distance from the macula lutea increases. In order totake advantage of this knowledge, users need to know the distance fromthe macula lutea to the region being analyzed.

Due to the smaller view angle of AO-SLO images than SLO images, anAO-SLO image, which is obtained by imaging the region in which thedensity is to be evaluated, does not generally include the macula lutea.Thus, it is difficult to know the exact distance from the macula luteato the region. Accurate evaluation of the photoreceptor cells istherefore difficult to achieve.

SUMMARY OF THE INVENTION

There is provided an image processing apparatus including an alignmentunit configured to align a first fundus oculi image that is anaberration-corrected image of an eye being examined and a second fundusoculi image that is an image having a larger view angle and a lowerresolution than the first fundus oculi image, by using a third fundusoculi image that is an image having a smaller view angle and a higherresolution than the second fundus oculi image; a distance acquisitionunit configured to acquire a distance from a macula lutea of the eyebeing examined to a certain position in the first fundus oculi imagealigned by the alignment unit; and an evaluation unit configured toevaluate the state of the eye being examined from the distance andinformation concerning photoreceptor cells included in the first fundusoculi image.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of a functionalconfiguration of an image processing apparatus according to a firstexemplary embodiment.

FIG. 2 is a flowchart illustrating an example of the processingprocedure of the image processing apparatus according to the firstexemplary embodiment.

FIG. 3 is a diagram illustrating an example of a fixation lamp map usedto operate the position at which a fixation lamp is placed.

FIG. 4 is a diagram illustrating an example of a captured WF-SLO imageand AO-SLO image group.

FIG. 5 is a flowchart illustrating an example of an alignment processillustrated in FIG. 2.

FIG. 6 is a diagram illustrating an example of a superimposition imageof the WF-SLO image and the AO-SLO image group which have been alignedin position.

FIG. 7 is a flowchart illustrating an example of a photoreceptor-cellanalysis process illustrated in FIG. 2.

FIGS. 8A and 8B are diagrams illustrating an example of the selection ofregions.

FIGS. 9A and 9B are diagrams illustrating an example of the relationshipbetween a normal-eye distribution and detection results.

FIGS. 10A and 10B are diagrams illustrating an example of a deviationfrom the normal-eye distribution.

FIG. 11 illustrates an example in which images indicating an example ofdeviations from the normal-eye distribution are displayed so as to beplaced on the superimposition image.

FIG. 12 is a flowchart illustrating an example of the processingprocedure of an image processing apparatus according to a secondexemplary embodiment.

FIG. 13 is a diagram illustrating an example of a lesion that appears ona superimposition image.

FIG. 14 is a diagram illustrating an example in which images indicatingdeviations from the normal-eye distribution are displayed so as to beplaced on the superimposition image.

DESCRIPTION OF THE EMBODIMENTS

Some exemplary embodiments will be described in detail hereinafter withreference to the drawings.

Exemplary Embodiments First Exemplary Embodiment

In a first exemplary embodiment, an AO-SLO apparatus acquires a capturedretinal image by imaging the region in which the density ofphotoreceptor cells is to be evaluated and a range including the regionof the macula lutea, and acquiring the distance from the macula lutea tothe region in which the density is to be evaluated. The AO-SLO apparatusfurther detects the photoreceptor cells using an algorithm that reflectsthe distance from the macula lutea, and acquires the density ofphotoreceptor cells as a function of the distance from the macula lutea.The above processes will be described hereinafter.

Specifically, images are captured for the position of the macula luteaand for positions at distances of 0.5 mm and 1.0 mm from the maculalutea with changing fixation, at different resolutions. The distancesfrom the macula lutea are illustrative, and are not limited to thevalues described above.

An aberration-corrected SLO image is a high-resolution image which isobtained by imaging a small range. Thus, a plurality of locations areimaged for each eye being examined. As a result of the imaging of asingle location, a plurality of images specified by durations of imagingand frame rates are acquired. Hereinafter, a group of images obtained bythe imaging of a single location will be sometimes referred to as“AO-SLO images”. Since AO-SLO images can be captured with changingimaging ranges, AO-SLO images having different resolutions can beacquired. Furthermore, AO-SLO images for a plurality of locations havingdifferent resolutions, which are captured for each eye being examined,will be sometimes referred to as an “AO-SLO image group” for the eyebeing examined.

Through the alignment between AO-SLO images for the eye being examined,the positional relationship therebetween is established. Then, themacula lutea is detected from AO-SLO images in which the macula luteaappears, and the distance from the macula lutea to the region of eachAO-SLO image is determined while the eye's axial length is taken intoaccount. The photoreceptor cells are detected using AO-SLO images havingsufficient resolutions, and an index such as the density ofphotoreceptor cells is acquired. Accordingly, the density ofphotoreceptor cells may be acquired as a function of the distance fromthe macula lutea.

One method taking the eye's axial length into account is described in,for example, the following publication: Bennett A G, Rudnicka A R, EdgarD F, Improvements on Littmann's method of determining the size ofretinal features by fundus photography, Graefes Arch Clin ExpOphthalmol, June 1994, 232 (6): 361-7. Specifically, the followingcalculation may be performed: L=q θ, q=0.01306 (x−1.82), where L denotesthe distance from the macula lutea to a given position on the fundusoculi, θ denotes the oscillation angle of the scanner, and x denotes theeye's axial length. The method taking the eye's axial length intoaccount is not limited to the method described above, and any othermethod may be used. For example, the method described in the followingpublication may be used: Li K Y, Tiruveedhula P, Roorda A, IntersubjectVariability of Foveal Cone Photoreceptor Density in Relation to EyeLength Invest Ophthalmol Vis Sci., December 2010, 51 (12): 6858-67.

In this way, the use of a calculated distance from the macula luteaallows more accurate detection of photoreceptor cells. In addition, thedensity of photoreceptor cells, which is known to change with thedistance from the macula lutea, may be acquired in association with thedistance from the macula lutea.

Configuration of Image Processing Apparatus

FIG. 1 illustrates an example of a functional configuration of an imageprocessing apparatus 10 according to this exemplary embodiment.

In FIG. 1, an image acquisition unit 100 acquires AO-SLO images thathave been acquired by an AO-SLO apparatus. The acquired AO-SLO imagesare stored in a storage unit 130 through a control unit 120. The imageacquisition unit 100 may acquire AO-SLO images by merely receivingAO-SLO images transmitted from the AO-SLO apparatus or by fetching, byitself, AO-SLO images saved in a memory or the like provided outside theimage processing apparatus 10. That is, the term “acquire” or“acquisition”, as used herein, at least includes mere reception andactively going to an AO-SLO apparatus to retrieve something.

An input information acquisition unit 110 acquires user input. An imageprocessing unit 140 includes a position alignment unit 141, a distancecalculation unit 142, a photoreceptor-cell analysis unit 143, and acomparison unit 144.

The image processing unit 140 performs alignment between the acquiredAO-SLO images to determine the relative positions of the AO-SLO images,and acquires the distance from the macula lutea to a region beinganalyzed in accordance with the positional relationship between anAO-SLO image used to detect the macula lutea and an AO-SLO image to beanalyzed. The image processing unit 140 performs photoreceptor-cellanalysis while reflecting the acquired distances from the macula lutea,and calculates an index such as density.

Further, the image processing unit 140 compares the calculated indexwith normal-eye data saved in the storage unit 130, and generates agraph or an image that clearly represents the comparison results.

An output unit 150 outputs the calculated index or the comparisonresults to a display unit such as a monitor, and also outputs theprocessing results stored in the storage unit 130 to a database.

Processing Procedure of Image Processing Apparatus

Next, an example of the processing procedure of the image processingapparatus 10 according to this exemplary embodiment will be describedwith reference to a flowchart illustrated in FIG. 2.

Step S210

In step S210, the image acquisition unit 100 acquires a plurality ofAO-SLO images of, for example, the retina of the eye being examined froman AO-SLO apparatus connected to the image processing apparatus 10.

An example of an imaging method for an eye being examined in order toperform photoreceptor-cell analysis will now be given. The AO-SLOapparatus used in this exemplary embodiment changes the position of afixation lamp included in the AO-SLO apparatus so that the eye beingexamined may be imaged while gazing at different locations to obtaincaptured images of different locations in the retina. FIG. 3 illustratesan example of a fixation lamp map used to operate the position at whichthe fixation lamp is place. The fixation lamp map is displayed on, forexample, the display unit. An examiner or user is able to change theposition at which the fixation lamp is turned on to the desired positionusing a pointing device (or pointer) such as a mouse while referring tothe displayed fixation lamp map.

In an example of the operation, first, the fixation lamp is placed whilethe center in the fixation lamp map illustrated in FIG. 3 is selected.The center position will be hereinafter sometimes referred to as a“reference position”. The eye being examined may be imaged while gazingat the fixation lamp placed at the reference position to obtain acaptured image of the region around the macula lutea.

In this state, a wide-field SLO (WF-SLO) image and a plurality of AO-SLOimages having different resolutions are captured using the AO-SLOapparatus. The captured WF-SLO image is acquired by the imageacquisition unit 100.

The WF-SLO image may have an image size of 8 mm×6 mm and a size of533×400 pixels, by way of example. A wide range of the retina is imagedto acquire an entire image of the retina. The correspondence between theWF-SLO image and the AO-SLO images having small view angles allows theuser to identify which location in the retina each of the AO-SLO imagecorresponds to.

The AO-SLO images are captured at three levels of resolution with imagedarea sizes of 1.7 mm×1.7 mm, 0.82 mm×0.82 mm, and 0.34 mm×0.34 mm whilea size of 400×400 pixels is commonly used. These images are capturedusing the AO-SLO apparatus, and the captured images are acquired by theimage acquisition unit 100. An AO-SLO image with an imaged area size of1.7 mm×1.7 mm will be sometimes referred to as an “L image”, an AO-SLOimage with 0.82 mm×0.82 mm as an “M image”, and an AO-SLO image with0.34 mm×0.34 mm as an “S image”. The imaged area sizes and the size ofpixels described above are illustrative, and are not limited to thevalues described above. In other words, other values may be used. The Mimage or S image corresponds to an example of a first fundus oculiimage. The WF-SLO image or L image corresponds to an example of a secondfundus oculi image, and the L image or M image corresponds to an exampleof a third fundus oculi image.

While the duration of imaging and frame rate of an AO-SLO image may bechanged, a frame rate of 32 frames per second and an imaging time of 2seconds may be used here and an AO-SLO image may be formed of 64 images,by way of example.

Then, the fixation is shifted to the superior 0.5 mm position, and theexaminee is instructed to gaze at the fixation lamp at the shiftedposition. In this state, M images and S images are captured. Likewise,the fixation is shifted to the nasal 0.5 mm position, to the inferior0.5 mm position, and to the temporal 0.5 mm position in this order, andM images and S images are captured. The order in which the fixation isshifted is not limited to the example described above, and the fixationmay be shifted in any other order. The same applies to the followingdescription.

The fixation is further shifted to the superior 1.0 mm position, and theexaminee is instructed to gaze at the fixation lamp at the shiftedposition. In this state, M images and S images are captured. Likewise,the fixation is shifted to the nasal 1.0 mm position, to the inferior1.0 mm position, and to the temporal 1.0 mm position in this order, andM images and S images are captured.

FIG. 4 schematically illustrates a WF-SLO image and an AO-SLO imagegroup which are captured for each eye being examined in the waydescribed above. In FIG. 4, the captured L, M, and S images aredisplayed so as to be superimposed on the WF-SLO image on the basis ofonly the information on the positions of the fixation.

As illustrated in FIG. 4, it may be found that the S images captured atthe 0.5 mm and 1.0 mm positions do not overlap, whereas the M imagescaptured at the 0.5 mm and 1.0 mm positions overlap. S images would bemore appropriate for better resolving of the photoreceptor cells.However, the imaging of the photoreceptor cells up to the 1.0 mm regionby using only S images so that the S images overlap one another involvesimaging at intervals of at least 0.3 mm, by way of example, which maylead to increased load on the examinee. For this reason, the locationsto be analyzed are set to the 0.5 mm and 1.0 mm positions, at which Simages are captured. In addition, M images are captured at the 0.5 mmand 1.0 mm positions of fixation so that the M images at least partlyoverlap. The overlapping of the M images allows the user to accuratelyidentify the mutual positions of the M images, thereby allowing the userto accurately identify the positions of the S images.

If characteristic objects such as thick blood vessels appear in an Simage, the S image may be directly associated with the WF-SLO imagewithout capturing M images and/or L images, or may be associated withthe WF-SLO image using either M images or L images. However, it may begenerally difficult to accurately associate the S image with the WF-SLOimage because the WF-SLO image has a lower resolution than AO-SLOimages. Capturing a combination of aberration-corrected SLO imageshaving a lower resolution than S images, such as M images or L images,enables accurate alignment with a reduced load on the examinee.

The acquired AO-SLO image group of the eye being examined is saved inthe storage unit 130 through the control unit 120.

Step S220

In step S220, the input information acquisition unit 110 selects areference frame from among the frames constituting an AO-SLO image savedin the storage unit 130.

As described with reference to step S210, by way of example, an AO-SLOimage is constituted by 64 frames which are obtained by imaging the samelocation for 2 seconds. Because of the small movements of the eye beingexamined during fixation, shifts may occur in the imaging positions ofthe 64 frames, and a distortion may also occur in each frame. The userselects a frame that has a small distortion and that is in good imagingcondition from among the 64 frames to obtain a reference frame.

While the user selects a reference frame by way of example, a referenceframe may be selected by using software. For example, average values orvariances of brightness may be calculated, and a frame having a largeaverage value or variance may be selected. Alternatively, a frame inwhich a ring structure indicating the presence of the photoreceptorcells is clearly observed in the frequency analysis may be selected.

The reference frames selected for the respective AO-SLO images in themanner described above are saved in the storage unit 130 through thecontrol unit 120.

Note that if a single AO-SLO image is captured at each position offixation in step S210, the processing of step S220 is not executed.

Step S230

In step S230, which is an example of an aligning step, the positionalignment unit 141 performs mutual alignment between the plurality ofAO-SLO images acquired by the AO SLO apparatus, which are saved in thestorage unit 130. The alignment is performed using the reference framesselected for the respective AO-SLO images.

Since images having similar resolutions are aligned with high accuracy,for example, the WF-SLO image and the L images may be first aligned, andthen the L images and the M images, followed by the M images and the Simages, are aligned. There may be various alignment methods. Here, thephase-only correlation (POC) method is used by way of example. Thedetails of alignment between the M image and the S image which arecaptured at the same position of fixation will be described withreference to a flowchart illustrated in FIG. 5.

Here, alignment is carried out between images having differentresolutions. An image having a low resolution and a large view angle isreferred to as a “large image”, and an image having a high resolutionand a small view angle is referred to as a “small image”. Specifically,in the case of alignment between an M image and an S image, the M imageis a large image and the S image is a small image. In the case ofalignment between an L image and an M image, the L image is a largeimage and the M image is a small image.

Step S510

In step S510, the position alignment unit 141 sets a pair of two AO-SLOimages between which alignment is to be performed from among theplurality of AO-SLO images acquired in step S210. There may be varioussetting methods. For example, first, the WF-SLO image and an L image atthe reference position are selected as a pair. Then, the L image and anM image at the reference position are selected. Furthermore, the L imageand M images at the four 0.5 mm positions (in the superior, nasal,inferior, and temporal directions) of fixation are selected.

After that, M images at the superior 0.5 mm and 1.0 mm positions areselected. Likewise, M images at the nasal, inferior, and temporal 0.5 mmand 1.0 mm positions are selected. Finally, an M image and an S image atthe same position of fixation are selected.

The pairs of AO-SLO images set in the manner described above are savedin the storage unit 130 through the control unit 120. The proceduredescribed above is an example, and images may be selected in any otherorder.

Step S520

In step S520, the position alignment unit 141 acquires the pairs ofimages selected in step S510 from the storage unit 130 through thecontrol unit 120. The following description will be given in detail ofan example in which acquired two images have different resolutions, forexample, an example in which a large image is an M image and a smallimage is an S image at the same position of fixation.

An M image has an image size of approximately 820 μm, and an S image hasan image size of approximately 340 μm, each of which has a size of400×400 pixels. In these conditions, an M image overlaps an S image at acenter thereof in the region of approximately 166 pixels. In the POCmethod, the fast Fourier transforms (FFTs) of the respective images arecomputed, and thus a region of 128×128 pixels in the center of the Mimage is cropped for high-speed operation. The size of the region to becropped is not limited to the value described above, and may be a sizeto allow a large image and a small image to overlap. In a case where anL image and an M image are selected as a pair, for example, the centerof the M image which is aligned based on the position of fixation is setas the center of the region to be cropped from the L image.

Step S530

In step S530, the position alignment unit 141 performs resolutionconversion on the region in the small image among the image pairacquired in step S520 which corresponds to the region of the large imagecropped in step S520. Specifically, an image of 128×128 pixels isgenerated using a region of 262.4 μm, which is given by 820×128/400,from the center of the S image. That is, the position alignment unit 141corresponds to an example of a resolution conversion unit configured toconvert the resolution of a high-resolution image into a resolution thatis identical to the resolution of a low-resolution image in a case wherean alignment unit performs alignment. The processing of step S520 andthe processing of step S530 may be executed in opposite order, or may beexecuted simultaneously.

Step S540

In step S540, the position alignment unit 141 calculates an amount ofshift for the portion (cropped large image) of the large image which hasbeen cropped in step S520 and for the small image (low-resolution smallimage) whose resolution has been converted in step S530, using, forexample, the POC method. Specifically, the cropped large image and thelow-resolution small image are frequency-converted by FFTs. If thefrequency-converted images are represented by F(u, v) and G(u, v), theposition alignment unit 141 computes a phase-only correlationcoefficient C(u, v) using the equation below.

${C( {u,v} )} = \frac{{F( {u,v} )}{G( {u,v} )}^{*}}{{{F( {u,v} )}{G( {u,v} )}^{*}}}$

That is, the position alignment unit 141, which is an example of thealignment unit, performs alignment using the resolution-convertedimages.

The inverse FFT of C(u, v) is determined, and a peak value is detected.Accordingly, amounts of shift may be calculated.

The calculated amounts of shift are saved in the storage unit 130through the control unit 120.

Step S550

In step S550, the position alignment unit 141 determines whether theamounts of shifts for all the image pairs set in step S510 have beencalculated. If there is any image pair for which the amount of shift hasnot been calculated, the process returns to step S520, in which anamount of shift for the image pair is calculated. If the processing hasbeen completed for all the image pairs, the process proceeds to stepS560.

Step S560

In step S560, the position alignment unit 141 performs mutual alignmentbetween images in each of the image pairs set in step S510 in accordancewith the corresponding amount of shift calculated in step S540.

Specifically, the amount of shift (or the amount of displacement)between the WF-SLO image and the L image, which are captured at thereference position, the amount of shift between the L image and the Mimage, and the amount of shift between the M image and the S image areused to determine the amounts of shift of the L, M, and S images withrespect to the WF-SLO image. Also, the amount of shift between the Limage and the M image at the 0.5 mm position of fixation and the amountof shift between the M image and S image at the 0.5 mm position offixation are used to determine the amounts of shift of the M and Simages at the 0.5 mm position of fixation with respect to the WF-SLOimage.

The mutual positions of all the AO-SLO images are determined in a waysimilar to that described above, and a superimposition image in whichimages are superimposed on one another so that, for example, imageshaving large imaged areas underlie images having small imaged areas atcorresponding positions is generated. That is, the position alignmentunit 141 aligns the images in accordance with the calculated amounts ofshift. FIG. 6 illustrates an example of a superimposition image in whichthe AO-SLO image group illustrated in FIG. 4 is mutually aligned. Thesuperimposition image illustrated in FIG. 6 is displayed on the displayunit such as a monitor using the output unit 150. Specifically, theoutput unit 150 causes a first image and a second image which arealigned by an alignment unit to be displayed on a display unit in such amanner that the first image and the second image are superimposed oneach other. In FIG. 4, since the AO-SLO image group is placed on thebasis of only the positions of fixation, blood vessels and the likedeviate in position. In FIG. 6, in contrast, images having differentresolutions are used to perform alignment stepwise, resulting in theAO-SLO images being placed at the correct positions. That is, theposition alignment unit 141 aligns the WF-SLO image and the S imagesthrough the M images and the L images. The position alignment unit 141corresponds to an example of an alignment unit configured to align afirst fundus oculi image that is an aberration-corrected image of theeye being examined and a second fundus oculi image having a larger viewangle and a lower resolution than the first fundus oculi image by usinga third fundus oculi image having a smaller view angle and a higherresolution than the second fundus oculi image. More specifically, theposition alignment unit 141, which is an example of the alignment unit,aligns a plurality of first fundus oculi images and a plurality ofsecond fundus oculi images, which are obtained by imaging differentpositions on the fundus oculi of the eye being examined, by using aplurality of third fundus oculi images that are obtained by imaging thedifferent positions on the fundus oculi. In a different aspect, theposition alignment unit 141, which is an example of the alignment unit,aligns a first fundus oculi image and a second fundus oculi image inaccordance with the result of alignment between the second fundus oculiimage and a third fundus oculi image and the result of alignment betweenthe third fundus oculi image and the first fundus oculi image.

While an example of a method for generating a superimposition image byusing software has been described, positions may be manually aligned. Ina specific method, in the image illustrated in FIG. 4 in which AO-SLOimages are placed on the basis of the positions of fixation, thepositions of the AO-SLO images are shifted by clicking and dragging amouse while referring to characteristic objects such as blood vessels.Alignment may be performed manually only, or alignment may be performedby using software in the manner described above and then aligned imagesmay be modified manually. In this case, for example, a pair of imagesconsisting of an M image and an S image which are captured at the sameposition of fixation may be aligned first, and then the pair of alignedimages may be aligned with the WF-SLO image and an L image.

The acquired superimposition image of the AO-SLO image group is saved inthe storage unit 130 through the control unit 120. After thesuperimposition image is displayed on a monitor or the like through theoutput unit 150, the process returns to step S230.

Step S240

In step S240, the input information acquisition unit 110 acquires theposition of the macula lutea from the superimposition image generated instep S220. The input information acquisition unit 110 saves the detectedposition of the macula lutea in the storage unit 130 through the controlunit 120. The input information acquisition unit 110 corresponds to anexample of a detection unit configured to detect the macula lutea.

There may be a plurality of possible methods for detecting the maculalutea. In one possible detection method, a user visually detects themacula lutea in an S image captured to be located at the center offixation. This detection method is based on the knowledge that thebrightness tends to decrease toward the region of the macula lutea, andmay thus be implemented by software. In a specific method, for example,the differences between the brightness values of the respective pixelsand the highest brightness value in the image are determined, and thecentroid of the differences is determined and used as the macula lutea.

In another possible method, the center of an S image located at thecenter of fixation is used as the macula lutea since imaging isperformed while the examinee is gazing at the fixation. That is, theinput information acquisition unit 110, which is an example of thedetection unit, detects the macula lutea from a first fundus oculi imageincluding the macula lutea among a plurality of first fundus oculiimages aligned by the alignment unit.

Here, AO-SLO images and a WF-SLO image are used. If it is possible toacquire other modality images such as optical coherence tomography (OCT)images obtained by imaging the same eye being examined, AO-SLO imagesand a WF-SLO image may be compared with these images to allow the userto select a position considered to be the macula lutea.

Since an S image has a higher resolution than any other image, it ispossible to accurately detect the macula lutea. The macula lutea mayalso be detected using M images, L images, or a WF-SLO image.

Step S250

In step S250, which is an example of a distance acquiring step, thedistance calculation unit 142 calculates the distances from the maculalutea to the regions of the respective AO-SLO images from the amounts ofshift of the respective AO-SLO images with respect to the WF-SLO image,which are calculated in step S230, and the position of the macula lutea,which is acquired in step S240. That is, the distance calculation unit142 corresponds to an example of a distance acquisition unit configuredto acquire the distance from macula lutea of the eye being examined to acertain position in the first fundus oculi image aligned by thealignment unit. More specifically, the distance calculation unit 142,which is an example of the distance acquisition unit, acquires thedistance from the macula lutea detected by the detection unit to acertain position in a first fundus oculi image not including the maculalutea among a plurality of first fundus oculi images aligned by thealignment unit.

The distance from the macula lutea to the region of each AO-SLO imagemay be determined by, for example, determining the coordinates of thecenter of the AO-SLO image, where the upper side of the superimpositionimage with respect to the macula lutea as a point of origin isrepresented by the Y-axis direction and the right side by the X-axisdirection. The acquired coordinates of each AO-SLO image are referred toas “macular coordinates”. The above distance is represented by D_(i)−M,where the amount of shift of each AO-SLO image i, which is determined instep S230, is represented by a vector D_(i) and the position of themacula lutea acquired in step S240 is represented by a vector M from thecenter of the WF-SLO image. If the coordinates of the center of theAO-SLO image i are represented by (X_(i), Y_(i)), the distance R_(i)from the macula lutea to the center of the AO-SLO image i may bedetermined by

R _(i)=√{square root over (X _(i) ² +Y _(i) ²)}.

The determined macular coordinates of the respective AO-SLO images andthe distances from the macula lutea are saved in the storage unit 130through the control unit 120.

Step S260

In step S260, the photoreceptor-cell analysis unit 143 performsphotoreceptor-cell analysis on the S images in the AO-SLO imagesacquired in step S210.

FIG. 7 is a flowchart illustrating the details of the photoreceptor-cellanalysis process.

Step S710

In step S710, the photoreceptor-cell analysis unit 143 performspreprocessing for photoreceptor-cell analysis on the basis of thereference frames of the AO-SLO images acquired in step S220. There maybe a plurality of methods for the preprocessing. Here, noise reductionbased on frequency analysis is given herein. Specifically, the referenceframes are subjected to frequency conversion, and are subjected toinverse conversion after the application of a filter that eliminates thehigh-frequency component.

Since it is known that the photoreceptor cell size is approximately 2 μmaround the region of the macula lutea, which is the smallest (thehighest density of photoreceptor cells), the cut-off value (cut-offfrequency) for removing the high-frequency component as noise is definedso that vibrations with a period shorter than 2 μm may be removed. Sinceit is also known that the density of photoreceptor cells decreases withincreasing distance from the macula lutea, the cut-off frequency ischanged in accordance with the distances acquired in step S250. Forexample, the photoreceptor cell size is defined to be 2 μm in the centerof the region of the macula lutea, increasing by 1 μm every 0.5 mm ofthe distance from the macula lutea in the range from the center of themacula lutea to the 1.0 mm position, and to be 4 μm (fixed) at positionsfarther than the 1.0 mm position with respect to the macula lutea, andthe cut-off frequency is determined in accordance with the distancesfrom the macula lutea. That is, the photoreceptor-cell analysis unit143, which is an example of the detection unit, changes a parameter tobe used to detect the photoreceptor cells in accordance with thedistances acquired by the distance acquisition unit.

Another method for noise reduction is to use a plurality of framesacquired as AO-SLO images to superimpose the AO-SLO images on oneanother. Specifically, after 64 frames of each AO-SLO image aresubjected to registration processing by affine transformation or thelike, averaging processing is performed on the region corresponding tothe reference frame. The accuracy of this technique depends on theaccuracy of the registration processing. The registration processingdescribed above is followed by the removal of the high-frequencycomponent based on frequency conversion described above.

The acquired images may be referred to as “preprocessed images”.

Step S720

In step S720, the photoreceptor-cell analysis unit 143 detects thephotoreceptor cells from the preprocessed images acquired in step S710.The photoreceptor-cell analysis unit 143 corresponds to an example of adetection unit configured to detect the photoreceptor cells from a firstfundus oculi image.

Specifically, there is available a method for detecting local maximumvalues of the brightness of the preprocessed images. In this case, ifthe distance between points detected as local maximum values is smallerthan the photoreceptor cell size, which is known as knowledge, it isdetermined that detection is influenced by noise and the detected pointsmay be combined to increase robustness of detection. The photoreceptorcell size, used herein, may be calculated based on, similarly to stepS710, the distance from the macula lutea to the region of the AO-SLOimage to be analyzed, which is determined in step S250, to implementmore accurate detection.

Among the detected points acquired in the manner described above, pointshaving values greater than or equal to a specified threshold value areregarded as photoreceptor cells. The threshold value may be implementedas the lowest brightness value in an image (all the detected points areregarded as photoreceptor cells), the average brightness value in animage, or any other suitable value.

While an example of photoreceptor cell detection has been given here,the detection method is not limited to that described above and variousmethods are available. One possible method is to, for example, selectpixels having brightness values greater than or equal to a thresholdvalue to determine the centroid of a region where the selected pixelsare connected. Another possible method is to calculate features of asub-region and to detect photoreceptor cells using a pattern recognitiontechnique. Specifically, Gabor features are calculated for a sub-regionof, for example, 11×11 pixels. Gabor feature vectors that are obtainedfrom a plurality of sub-regions centered on a detected point regarded asa photoreceptor cell and a plurality of sub-regions not includingdetected points regarded as photoreceptor cells are used to performlearning based on the support vector machine. Gabor features arecalculated for a new target sub-region, and the results of the learningdescribed above are used to determine whether the center of thesub-region is a photoreceptor cell or not.

In addition to the software-based detection described above, some of thedetected points may be manually modified by a user. In this case, theinput information acquisition unit 110 acquires the position of adetected point modified by the user among the detected points obtainedin step S720.

Step S730

In step S730, the photoreceptor-cell analysis unit 143 performs Voronoianalysis on the detected points obtained in step S720.

Specifically, the following processing is performed on all the detectedpoints obtained in step S720: An image is divided into regions by theperpendicular bisectors of close detected points to calculate Voronoiregions each including every detected point.

Step S740

In step S740, the photoreceptor-cell analysis unit 143 selects a regionwhere an index is to be calculated from the results of the analysis insteps S720 and S730.

FIG. 8A illustrates an example of the selection of a region at thesuperior 0.5 mm position illustrated in FIG. 6, and FIG. 8B illustratesan example of the selection of a region at the temporal 0.5 mm positionillustrated in FIG. 6. The regions may be rectangular, polygonal, oval,or of any other desired shape, and are set by the photoreceptor-cellanalysis unit 143 in accordance with the user's click of the mouse orany other suitable operation. That is, the photoreceptor-cell analysisunit 143 corresponds to an example of a setting unit configured to set aregion in a first fundus oculi image. The photoreceptor-cell analysisunit 143 may automatically set a region instead of in accordance with auser operation. After a region is automatically set, the region may bechanged in accordance with a user operation.

If a blood vessel or the like is included in a region, the portion belowthe blood vessel has a low brightness, resulting in low accuracy in thedetection of the photoreceptor cells. Accordingly, a region is selectedso as to avoid including a blood vessel. That is, the photoreceptor-cellanalysis unit 143, which is an example of the setting unit, sets aregion so as not to include a blood vessel region which is included in afirst fundus oculi image.

Furthermore, since the density of photoreceptor cells depends on theirdistance from the macula lutea, a landscape (or horizontally oriented)region is selected as a superior region, and a portrait (or verticallyoriented) region is selected as a temporal region. Inferior and nasalregions are also selected in a similar way, that is, a landscape regionis selected as an inferior region and a portrait region is selected as anasal region. Accordingly, the variations of the distance from themacula lutea to a photoreceptor cell included in the same region may bereduced. That is, the photoreceptor-cell analysis unit 143 sets a regionso that the width in the direction perpendicular to a straight lineconnecting an S image and the macula lutea is larger than the width inthe direction parallel to the straight line. That is, thephotoreceptor-cell analysis unit 143, which is an example of the settingunit, changes a method for setting the region in accordance with thepositional relationship between a first fundus oculi image and themacula lutea. More specifically, the photoreceptor-cell analysis unit143, which is an example of the setting unit, sets the region so thatthe width in the direction perpendicular to a straight line connecting afirst fundus oculi image and the macula lutea is larger than the widthin the direction parallel to the straight line.

The region may be set by, as described above, selecting an area having acertain size from a preprocessed image, or by setting a selected regionfor each detected point. For example, a circle with a size of 20 μmwhich is centered on each detected point may be set, and portions in thecircles may be regarded as regions corresponding to the respectivedetected points. Regions may also be set in units of pixels at certainintervals without using detected points. Also in this case, for example,a circle with a size of 20 μm may be set for each of pixels selected atintervals of 10 pixels in the vertical and horizontal directions, andregions included in the circles may be used as selected regions.

Further, the distances from the macula lutea to the selected regions arecalculated. The positions of the regions of the AO-SLO images from themacula lutea are calculated in step S250. The positions of the regionsof the AO-SLO images calculated in step S250 and the positions of theselected regions in the AO-SLO images are used to calculate the distancefrom the macula lutea to the center of each of the selected regions.That is, the distance calculation unit 142, which is an example of thedistance acquisition unit, acquires the distance from the position ofthe macula lutea to a certain position in a region (for example, thecenter of the region).

Step S750

In step S750, the photoreceptor-cell analysis unit 143 calculates anindex for each of the regions selected in step S740 in accordance withthe results acquired in steps S720 and S730.

Examples of the index include the number of detected points (the numberof photoreceptor cells) determined in step S720, the density (thedensity of the photoreceptor cells) obtained by dividing the number ofdetected points by the area of the region, the average area per detectedpoint in each of the selected regions, the distance to the nearestdetected point determined in step S730, and the ratio of hexagonalVoronoi regions to the others. In addition to the index described above,the lateral width, longitudinal width, and area of the regions may bedisplayed. Additionally, the ideal distance to the nearest detectedpoint, the ratio of the actual distance to the nearest detected point tothe distance to the nearest detected point, and the like may also bedisplayed. The distance from the macula lutea may also be displayed.

The results of the photoreceptor-cell analysis and the acquired index orindices are saved in the storage unit 130 through the control unit 120.Then, the process returns to step S260.

Step S270

In step S270, which is an example of an evaluating step, the comparisonunit 144 compares an index acquired as a result of thephotoreceptor-cell analysis in step S260 with the normal-eye data savedin the storage unit 130, which is an example of reference information.

FIG. 9A illustrates an example of the relationship in the normal eyebetween the density of photoreceptor cells, which is an example ofinformation concerning the photoreceptor cells, and the distance fromthe macula lutea. The shaded range in the illustrated graph representsthe range of variations of the normal eye. FIG. 9B illustrates anexample of the plot of the index of the density of photoreceptor cellsfor the selected regions illustrated in FIG. 8B, which is acquired instep S260. As illustrated in FIG. 9B, the normal-eye data correspondingto the distance from the macula lutea and the detected density ofphotoreceptor cells are compared. As may be seen, six regions out of theselected regions are included in the normal range and one regiondeviates from the normal range. That is, the comparison unit 144corresponds to an example of an evaluation unit configured to evaluatethe state of the eye being examined from the distance from the maculalutea to a certain position in a first fundus oculi image andinformation concerning photoreceptor cells included in the first fundusoculi image. More specifically, the comparison unit 144, which is anexample of the evaluation unit, evaluates the state of the eye beingexamined from the distance to a certain position in a region andinformation concerning photoreceptor cells in the region. As describedabove, furthermore, the comparison unit 144, which is an example of theevaluation unit, compares the information concerning the photoreceptorcells with reference information based on the distance.

If a region deviates from the normal range, the output unit 150 causes aregion deviating from the normal range among the regions illustrated inFIG. 8B to be displayed on the display unit such as a monitor in amanner illustrated in FIG. 10A in order to clearly identify which regiondeviates from the normal range. That is, the output unit 150 correspondsto an example of a display control unit configured to cause a firstfundus oculi image to be displayed on a display unit in display formbased on a result of the comparison. For example, the graph illustratedin FIG. 9B and the regions illustrated in FIG. 10A are displayed on thedisplay unit side by side, allowing the user to easily identify a valuedeviating from the normal range and the corresponding one of theselected regions.

The graph illustrated in FIG. 9B and the regions illustrated in FIG. 8Bmay be linked with each other. The diagrams illustrated in FIGS. 8B and9B may be displayed on the display unit side by side, and, for example,a point on the graph illustrated in FIG. 9B may be clicked on, therebyimplementing edge enhancement of the corresponding one of the regionsillustrated in FIG. 8B. Accordingly, the correspondence relationship maybe more clearly identified.

Furthermore, in the selection of regions in step S740, selected regionsmay be set in units of detected points or in units of pixels at certainintervals. For example, in a case where regions are selected atintervals of 10 pixels in the vertical and horizontal directions, eachof the regions can be compared with the normal-eye distribution. FIG.10B illustrates an example in which the above processing is performed onthe regions illustrated in FIG. 8B. In FIG. 10B, the regions aredisplayed in color gradations in accordance with the deviation from thenormal distribution average value in such a manner that regionsdeviating from the average value are displayed in dark color. Regionsdeviating more from the normal range may be displayed in lighter coloror may be displayed in darker color. That is, the output unit 150, whichis an example of the display control unit, changes the lightness of thefirst fundus oculi image in accordance with the difference between theinformation concerning the photoreceptor cells and the referenceinformation corresponding to the distances.

The output unit 150 may further measure the area of a region out of therange of the normal distribution illustrated in FIG. 9A, and present themeasurement result. That is, the output unit 150, which is an example ofthe display control unit, presents the area of a region in the firstfundus oculi image for which the difference between the informationconcerning the photoreceptor cells and the reference informationcorresponding to the distances is greater than or equal to a certainvalue.

FIG. 11 illustrates an example in which images generated for therespective AO-SLO images in the manner illustrated in FIG. 10B aredisplayed so as to be placed on the superimposition image illustrated inFIG. 6. The index indicating the degree of progress of the disease maybe, for example, as a function of the distance from the macula lutea,the ratio of the area of an abnormal region to the entire area. Imagesgenerated in the manner illustrated in FIG. 10A may be displayed so asto be placed on the superimposition image illustrated in FIG. 6.

Step S280

In step S280, the input information acquisition unit 110 determineswhether or not the user modifies the results of analysis and the resultsof comparison therebetween presented in step S270. In this case, theuser may modify the alignment of each of the AO-SLO images in step S230and the detection of the macula lutea in step S240.

Specifically, if it is determined that the alignment of an AO-SLO imagein step S230 is incorrect, the user may change the position of theAO-SLO image. The user may also change the position of the macula luteadetermined in step S240.

If no modification is to be performed by the user, the process proceedsto step S290. If modification has been performed, the process returns tostep S250, in which the distances are calculated again based on themodified position of the macula lutea and the modified position of eachAO-SLO image. Then, the subsequent processing is performed.

Step S290

In step S290, the control unit 120 saves the processing results storedin the storage unit 130, such as the calculated index and the comparisonresults, in a database.

With the configuration described above, a plurality of AO-SLO imagesacquired by an AO-SLO apparatus may be analyzed and an index may becalculated while the positional relationship with the macula lutea,which is not included in the same AO-SLO image, is taken into account.

Second Exemplary Embodiment

In the first exemplary embodiment, by way of example, a superimpositionimage created from a plurality of AO-SLO images having differentresolutions is used to acquire the distance from the macula lutea to theregion of the AO-SLO image to be analyzed, and analysis and evaluationthat reflect the acquired distance are performed.

In a second exemplary embodiment, analysis and evaluation are performedusing information on a lesion or the like detectable on asuperimposition image.

In the hereditary diseases of the photoreceptor cells, as is known inthe art, particular ring-shaped structures are observed in accordancewith the progress of the diseases. Such ring structures are difficult toidentify on high-resolution and low-view-angle images. Such ringstructures may be identified on low-resolution but high-view-angleimages. The following description will be given of a method forextracting the above-described structure from a high-view-angle imageand changing the processing to be performed on a high-resolution imageaccordingly.

The functional configuration of an image processing apparatus 10according to this exemplary embodiment is similar to that illustrated inFIG. 1, and a description thereof is thus omitted.

The processing procedure of the image processing apparatus 10 accordingto this exemplary embodiment will be described with reference to aflowchart illustrated in FIG. 12. The processing of steps S210, S220,S230, and S290 is substantially the same as that described in the firstexemplary embodiment, and a description thereof is thus omitted.

Step S1240

In step S1240, the input information acquisition unit 110 acquires theposition of the macula lutea and the position of a lesion, which aredetected by the user, in the superimposition image generated in stepS220. Then, the detected positions of the macula lutea and the lesionare saved in the storage unit 130 through the control unit 120.

The method for detecting the macula lutea is substantially the same asthat described above in the first exemplary embodiment. In a method fordetecting a ring structure, a user visually detects a ring structurefrom a superimposition image. FIG. 13 illustrates an example of asuperimposition image in which a ring structure appears. In FIG. 13,since the ring structure appears in a small region, a region created byfour L images including the ring structure within the superimpositionimage is illustrated in an enlarged view. A user clicks thesuperimposition image with a mouse along the outline of the illustratedring structure, thereby acquiring the outline.

There is still a method for acquiring a ring structure by extracting thering structure from an image such as a fundus autofluorescence (FAF)image and placing the ring structure at a corresponding position in thesuperimposition image while referencing the positions of blood vesselsand the like.

Step S1250

In step S1250, the distance calculation unit 142 calculates thecoordinates of the region of each AO-SLO image with respect to themacula lutea as a point of origin, from the amount of shift of theAO-SLO image from the WF-SLO image, which is calculated in step S230,and from the position of the macula lutea acquired in step S1240. Thedistance calculation unit 142 further calculates the coordinates of theposition of the lesion, which is acquired in step S1240, with respect tothe macula lutea as a point of origin.

The method for determining the coordinates of the region of each AO-SLOimage is substantially the same as the method described in the firstexemplary embodiment. The coordinates of the position of the lesion maybe acquired by converting the coordinate values acquired in step S1240by mouse click or the like into a length on the superimposition imageand by using the position of the macula lutea as a point of origin.

The determined AO-SLO images, and the position of the lesion from themacula lutea are saved in the storage unit 130 through the control unit120.

Step S1260

In step S1260, the photoreceptor-cell analysis unit 143 performsphotoreceptor-cell analysis on S images in the aberration-corrected SLOimages acquired in step S210.

The step of photoreceptor cell analysis is performed in accordance withthe method described above in the first exemplary embodiment. Thefollowing description will focus on the process for changing theprocessing in accordance with lesion information acquired in step S1240.

In general, the progress of the disease differs inside and outside thering structure, and thus the photoreceptor cells are rendered in adifferent way. Although whether a nearly normal region is located insideor outside the ring depends on the disease, the photoreceptor cellsgenerally degenerate in the region where the disease progresses and astructure other than the photoreceptor cells may be rendered in thisregion or a wide low-brightness region may be rendered in this region.For this reason, if the local maximum detection algorithm given in thefirst exemplary embodiment applies, a large amount of noise may bepicked up, resulting in a reduction in the robustness of the algorithm.

In order to avoid the above situation, if the region to be subjected tophotoreceptor-cell analysis is located on the side of the ring structurewhere the disease progresses, additional average filter processing isperformed in the preprocessing of step S710. While an average filter isused here, the type of filter to be used is not limited to an averagefilter and any type of filter that leads to a reduction in noise, suchas a Gaussian filter, may be used.

In addition, the threshold parameter used for the detection of step S720is changed inside or outside the ring. Specifically, for example, thelowest brightness value of the image is used inside the ring, as in thefirst exemplary embodiment, whereas the average brightness value of theimage is used outside the ring. The threshold parameter is changed inorder to, as described above, increase robustness of detection in a casewhere the photoreceptor cells are not clearly rendered inside or outsidethe ring.

Furthermore, the detection of step S720 is based on pattern recognition,by way of example. In this case, desirably, results of learning are usedfor inside and outside the ring for each disease.

Step S1270

In step S1270, the comparison unit 144 compares the index acquired as aresult of the photoreceptor-cell analysis in step S1260 with thenormal-eye data saved in the storage unit 130.

FIG. 14 illustrates results of processing similar to that illustrated inFIG. 11 which is performed on the respective S images illustrated inFIG. 13 through processing similar to that of step S270 in the firstexemplary embodiment. In FIG. 14, there is an inconsistency between thelesion appearing in the superimposition image and an image created bycomparing the results of the photoreceptor-cell analysis with thenormal-eye distribution. Specifically, the S images located on the rightside in FIG. 14 largely deviate from the normal distribution althoughthey are inside the ring structure, and the S images located on the leftside form a more normal distribution even though they are near the ringstructure. In this case, the positions of the respective S images in thesuperimposition image might not have been correctly located, or the ringstructure might not have been correctly acquired. Alternatively, whatactually appears as a ring structure in the superimposition image mightnot correctly reflect the actual lesion. The former inconvenience,caused by alignment or the acquisition of a ring structure, may beovercome in step S1280. In the latter case, however, analysis throughcomparison with other modality images is needed, and the imageillustrated in FIG. 14 is displayed to help the user elucidate the stateof the disease.

Step S1280

In step S1280, the input information acquisition unit 110 determineswhether or not the user modifies the results of analysis and the resultsof comparison therebetween presented in step S1270. In this case, theuser may modify the alignment of each of the AO-SLO images in step S230and the detection of the macula lutea and lesion in step S1240.

If it is determined that the user performs modification, the user resetsthe positions of the AO-SLO images and the positions of the macula luteaand the lesion. Then, the process returns to step S1250, in which thedistances are recalculated based on the modified positions of the maculalutea and the lesion and the modified positions of the AO-SLO images.Then, the subsequent processing is performed. If no modification isperformed by the user, the process proceeds to step S290.

OTHER EMBODIMENTS

Embodiments of the present invention can also be realized by a computerof a system or apparatus that reads out and executes computer executableinstructions recorded on a storage medium (e.g., non-transitorycomputer-readable storage medium) to perform the functions of one ormore of the above-described embodiment(s) of the present invention, andby a method performed by the computer of the system or apparatus by, forexample, reading out and executing the computer executable instructionsfrom the storage medium to perform the functions of one or more of theabove-described embodiment(s). The computer may comprise one or more ofa central processing unit (CPU), micro processing unit (MPU), or othercircuitry, and may include a network of separate computers or separatecomputer processors. The computer executable instructions may beprovided to the computer, for example, from a network or the storagemedium. The storage medium may include, for example, one or more of ahard disk, a random-access memory (RAM), a read only memory (ROM), astorage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2012-287252 filed Dec. 28, 2012, which is hereby incorporated byreference herein in its entirety.

What is claimed is:
 1. An image processing apparatus comprising: anacquisition unit configured to acquire a first fundus oculi image, asecond fundus oculi image and a third fundus oculi image, the firstfundus oculi image being an aberration-corrected image of an eye beingexamined, the second fundus oculi image being an image having a largerview angle and a lower resolution than the first fundus oculi image, thethird fundus oculi image being an image having a smaller view angle anda higher resolution than the second fundus oculi image; an alignmentunit configured to align a first fundus oculi image and a second fundusoculi image by using a third fundus oculi image.
 2. The image processingapparatus according to claim 1, wherein the alignment unit aligns thefirst fundus oculi image and the second fundus oculi image in accordancewith a result of alignment between the second fundus oculi image and thethird fundus oculi image and a result of alignment between the thirdfundus oculi image and the first fundus oculi image.
 3. The imageprocessing apparatus according to claim 1, further comprising: a displaycontrol unit configured to cause a display unit to display the firstfundus oculi image and the second fundus oculi image, wherein thedisplay control unit causes the first fundus oculi image and the secondfundus oculi image aligned by the alignment unit to be displayed on thedisplay unit in a such manner that the first fundus oculi image and thesecond fundus oculi image are superimposed on each other.
 4. The imageprocessing apparatus according to claim 2, further comprising: a displaycontrol unit configured to cause a display unit to display the firstfundus oculi image and the second fundus oculi image, wherein thedisplay control unit causes the first fundus oculi image and the secondfundus oculi image aligned by the alignment unit to be displayed on thedisplay unit in a such manner that the first fundus oculi image and thesecond fundus oculi image are superimposed on each other.
 5. The imageprocessing apparatus according to claim 1, wherein the third fundusoculi image is an aberration-corrected image of the eye being examined.6. The image processing apparatus according to claim 2, wherein thethird fundus oculi image is an aberration-corrected image of the eyebeing examined.
 7. The image processing apparatus according to claim 3,wherein the third fundus oculi image is an aberration-corrected image ofthe eye being examined.
 8. The image processing apparatus according toclaim 4, wherein the third fundus oculi image is an aberration-correctedimage of the eye being examined.
 9. The image processing apparatusaccording to claim 1, wherein the second fundus oculi image is not anaberration-corrected image of the eye being examined.
 10. The imageprocessing apparatus according to claim 2, wherein the second fundusoculi image is not an aberration-corrected image of the eye beingexamined.
 11. The image processing apparatus according to claim 3,wherein the second fundus oculi image is not an aberration-correctedimage of the eye being examined.
 12. The image processing apparatusaccording to claim 4, wherein the second fundus oculi image is not anaberration-corrected image of the eye being examined.
 13. The imageprocessing apparatus according to claim 5, wherein the second fundusoculi image is not an aberration-corrected image of the eye beingexamined.
 14. The image processing apparatus according to claim 6,wherein the second fundus oculi image is not an aberration-correctedimage of the eye being examined.
 15. The image processing apparatusaccording to claim 7, wherein the second fundus oculi image is not anaberration-corrected image of the eye being examined.
 16. The imageprocessing apparatus according to claim 8, wherein the second fundusoculi image is not an aberration-corrected image of the eye beingexamined.
 17. An image processing apparatus comprising: an acquisitionunit configured to acquire a first fundus oculi image, the first fundusoculi image being an aberration-corrected image of an eye being examineda detection unit configured to detect the photoreceptor cells from thefirst fundus oculi image, wherein the detection unit changes a parameterto be used to detect the photoreceptor cells in accordance with thedistance acquired by the distance acquisition unit.
 18. The imageprocessing apparatus according to claim 17, wherein the parameter is acut-off frequency of a filter which is applied to the first fundus oculiimage.
 19. The image processing apparatus according to claim 1, furthercomprising: a resolution conversion unit configured to convert theresolution of a high-resolution image into a resolution that isidentical to the resolution of a low-resolution image in a case wherethe alignment unit performs alignment, wherein the alignment unitperforms alignment using an image whose resolution has been converted.20. An image processing method comprising: acquiring a first fundusoculi image, a second fundus oculi image and a third fundus oculi image,the first fundus oculi image being an aberration-corrected image of aneye being examined, the second fundus oculi image being an image havinga larger view angle and a lower resolution than the first fundus oculiimage, the third fundus oculi image being an image having a smaller viewangle and a higher resolution than the second fundus oculi image;aligning a first fundus oculi image and a second fundus oculi image byusing a third fundus oculi image.